Copy the code and hyperparameters from galatea

README.md

@@ -1,4 +1,42 @@
-adversarial
-===========
+Generative Adversarial Networks
+===============================
 
-Code and hyperparameters for the paper "Generative Adversarial Networks"
+This repository contains the code and hyperparameters for the paper:
+
+"Generative Adversarial Networks." Ian J. Goodfellow, Jean Pouget-Abadie,
+Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville,
+Yoshua Bengio. ArXiv 2014.
+
+Please cite this paper if you use the code in this repository as part of
+a published research project.
+
+We are an academic lab, not a software company, and have no personnel
+devoted to documenting and maintaing this research code.
+Therefore this code is offered with absolutely no support.
+Exact reproduction of the numbers in the paper depends on exact
+reproduction of many factors,
+including the version of all software dependencies and the choice of
+underlying hardware (GPU model, etc). We used NVIDA Ge-Force GTX-580
+graphics cards; other hardware will use different tree structures for
+summation and incur different rounding error. If you do not reproduce our
+setup exactly you should expect to need to re-tune your hyperparameters
+slight for your new setup.
+
+Moreover, we have not integrated any unit tests for this code into Theano
+or Pylearn2 so subsequent changes to those libraries may break the code
+in this repository. If you encounter problems with this code, you should
+make sure that you are using the development branch of Pylearn2 and Theano,
+and use "git checkout" to go to a commit from approximately June 9, 2014.
+
+This code itself requires no installation besides making sure that the
+"adversarial" directory is in a directory in your PYTHONPATH. If
+installed correctly, 'python -c "import adversarial"' will work. You
+must also install Pylearn2 and Pylearn2's dependencies (Theano, numpy,
+etc.)
+
+parzen_ll.py is the script used to estimate the log likelihood of the
+model using the Parzen density technique.
+
+Call pylearn2/scripts/train.py on the various yaml files in this repository
+to train the model for each dataset reported in the paper. The names of
+*.yaml are fairly self-explanatory.